marketing research

Segmentation of buyers:

1. By age 2. By gender (men, women, kids)3. By income (low, middle, high)

By income level By savings level

4. By family size5. By lifestyle

Sportsmen Housewives Office workers/Freelancers/Unemployed Businessmen white collars/blue collars Travelers Fashionists

6. By nationality7. By race8. By religion9. By role

Fashion movers Influencers Decision makers Buyers End users

Segmentation of products:1. By season (winter, spring, summer, autumn, in-between)2. By material used3. By style4. By color5. By geographic region6. By brand7. By price8. By production method

Mass production Individual manufacture

9. By distribution method: Stores E-commerce (websites, social media, landing page By catalogue

10. By theme: Wedding Military Work shoes Medicine Big size etc.

11. By price orientation Standard price Stocked price (low cost brands, past year collections) Reduced price

12. By elasticity

Sales promotion of shoeware

I. Customization (3D printing, individual manufacture)II. Physical and nonphysical discounts (cumulative points;

buy 2, get 3rd free; buy 2 for a price of 1; holiday, special events price offs, birthday gifts; free complimentary goods; discount for a family of 4 people, for aged people; students cards, free shop cards after filling in questionnaire etc.)

III. Credit payment optionsIV. Huge warranty period (more than rivals offer)V. Aromamarketing (special fragrances in a showroom),

audiomarketing (surrounded music), tactile marketing (chocolates in a vase in a showroom, things that a potential buyer wants to touch), visual marketing (design of a showroom, showcases/racks).Warm light enhances the presence of leather goods, jeans and suites benefit from a cold light. It’s preferable

to have a ratio of 80% neutral colors against 20% of bright specific themed colors. Citrus fragrance arouses happiness, lavender and green tee alleviate and tranquilize, vanilla and ambergris – provide reliability and comfort, flower scents –romance and adventure. As for a tactile preferences wood is perfect for showcases.

VI. On screen video placed in a shop with new collection or/and fashion week shows demonstration.

VII. Social media stunts: Reviews in facebook, twitter, vkontakte, themed

media, review sites Coupons, promo actions Quizzes Feedback forms (upon a first run of a future

advertising, new collection involve target audience in a process of evaluation, advice in order to commence a dialogue with a potential customer as a primary goal.

Instagram as sale’s platform (5-6 hash tags underneath a photo)

VIII. Partner’s mutual benefit actions.(Buy and get a sales coupon in a massage saloon, romantic night in a restaurant for a couple, free ticket for movie, exhibition etc.)

IX. Neurolinguistic programming elements: The more efforts a buyer applies the more

valuable acquisition is (let a customer trade off; persuade him to buy a best bid; cause a frenzy, artificial shortage)

Product involving (when a customer participates in a product, when he can alter a product according to his preferences)

Creation of inferiority (you are short of smth..) Desire of distraction (selling goods at the airport

for instance to destruct people from upcoming flight)

Strategy “We are not finished yet and you will extra get…”

X. AdvertisingWith a live model. Highlights:

A model blends with environment by color and on contrary advertised shoe is underlined

Any action draws attention (crossing legs when sitting down) Elimination of competitor (only legs, feet are left)

Without a live model. Highlights: Addition of importance attributes (gypsum bust nearby – an element of

classical culture; flowers symbolize beauty, femininity and tenderness; wooden texture – closeness to nature; apples –temptation, horses – aristocratism, crocodiles, lizards and snakes – exotica; cats and leopards –elegance and grace

Action as well (flying sneaker for instance)

Examples of using SPSS IBM Statistics

1. Compare two means (Independent and two-paired t-tests)Initial data20 shoe shops in Dubai and Abu Dhabi, their selling records per week before and after test (for instance launch of promotive advertising campaign of louboutins)Null hypothesis1: Place of selling doesn’t affect the quantity of louboutins sold.Null hypothesis2: Advertising campaign failed and didn’t affect the sales.

Shops Region Pretest Posttest1 1,00 2000,00 2700,002 1,00 1400,00 1534,003 1,00 3455,00 3806,004 2,00 437,00 657,005 2,00 630,00 754,006 2,00 865,00 945,007 1,00 2567,00 3235,008 2,00 395,00 298,009 1,00 1234,00 2345,00

10 1,00 3214,00 5348,0011 1,00 5463,00 5550,0012 2,00 626,00 822,00

13 1,00 1235,00 1356,0014 2,00 371,00 426,0015 2,00 143,00 175,0016 2,00 68,00 79,0017 2,00 101,00 167,0018 2,00 352,00 406,0019 1,00 1111,00 1645,0020 2,00 631,00 743,00

Group Statistics

Region N Mean

Std. Deviatio

n

Std. Error Mean

Qty of louboutins sold per week

Dubai 9,00 2408,7 1445,82 481,94

Abu Dhabi11,00 419,9

1 252,87 76,24

Independent Samples Test

Levene's Test for Equality of

Variances t-test for Equality of Means

F Sig. t df

Sig. (2-

tailed)

Mean Differenc

e

Std. Error Differenc

e

95% Confidence Interval of the

Difference

Lower UpperQty of louboutins sold per week

Equal variances assumed

13,84 0,0016 4,51 18,00 0,000

3 1988,87 441,44 1061,45

2916,29

Equal variances not assumed

4,08 8,40 0,0032 1988,87 487,93 872,98 3104,7

6

As we can see from the tables Equal variances are not assumed (Sig <0.05) and null hypothesis1 can be rejected (Sig. 2 tailed<0.05).

Outcome1: Place of selling does affect the quantity of goods sold and mean value for Dubai is much larger that for Abu-Dhabi.

Paired Samples Statistics

Mean N

Std. Deviatio

n

Std. Error Mean

Pair 1 Qty of louboutins sold per week 1314,90 20,00 1394,40 311,80

Qty of louboutins sold per week 1649,55 20,00 1673,98 374,31

Paired Samples Correlations

NCorrelation Sig.

Pair 1 Qty of louboutins sold per week & Qty of louboutins sold per week 20,00 0,96 0,0000

Paired Samples Test

Paired Differences

t df

Sig. (2-

tailed)Mean

Std. Deviation

Std. Error Mean

95% Confidence Interval of the

Difference

Lower UpperPair 1 Qty of louboutens sold per

week - Qty of louboutins sold per week -334,65 516,83 115,57 -576,54 -92,76 -2,90 19,0

0 0,009

As we can see from the tables above two means differ unessential but t-test shows us that null hypothesis can be rejected (Sig. 2-tailed<0,05).

Outcome2: Advertising campaign affected the sales (increased it).

2. Compare two means in more than 2 clusters.Initial data4 shops, 10 brands sold in each shopNull hypothesis: all luxury brands contribute the same share into a total qty of goods sold.

Shop Brand Qty,pcs

Shop1 Gucci 3

Shop1 Miu-miu 6

Shop1 Stuart Weitzman 2

Shop1 Brain Atwood 7

Shop1Alexandra Mcqueen 4

Shop1 Walter Steiger 33

Shop1Christian Louboutin 2

Shop1 Jimmy Choo 2

Shop1 Manolo Blahnik 6

Shop1 Louis Voitton 12

Shop2 Gucci 3

Shop2 Miu-miu 5






Shop2 Jimmy Choo 7



Shop3 Gucci 6

Shop3 Miu-miu 7






Shop3 Jimmy Choo 7



Shop4 Gucci 5

Shop4 Miu-miu 7






Shop4 Jimmy Choo 12


Shop4 Louis Voitton 55Descriptives

Qty sold

N MeanStd.

DeviationStd. Error

95% Confidence Interval for Mean

Minimum MaximumLower Bound

Upper Bound

Gucci 4,00 4,25 1,50 0,75 1,86 6,64 3,00 6,00Miu-miu 4,00 6,25 0,96 0,48 4,73 7,77 5,00 7,00Stuart Weitzman 4,00 5,75 5,19 2,59 -2,51 14,01 1,00 12,00Brain Atwood 4,00 15,50 19,84 9,92 -16,07 47,07 2,00 45,00Alexandra Mcqueen 4,00 9,00 9,35 4,67 -5,87 23,87 4,00 23,00

Walter Steiger 4,00 22,00 7,96 3,98 9,34 34,66 14,00 33,00Christian Louboutin 4,00 7,50 10,34 5,17 -8,96 23,96 2,00 23,00

Jimmy Choo 4,00 7,00 4,08 2,04 0,50 13,50 2,00 12,00Manolo Blahnik 4,00 5,50 2,89 1,44 0,91 10,09 2,00 9,00Louis Voitton 4,00 36,75 18,12 9,06 7,92 65,58 12,00 55,00Total 40,00 11,95 13,32 2,11 7,69 16,21 1,00 55,00

Test of Homogeneity of Variances

Qtysold

Levene Statistic df1 df2 Sig.2,87 9,00 30,00 0,01

ANOVA

Qtysold

Sum of

Squares dfMean

Square F Sig.Between Groups 3813,90 9,00 423,77 4,10 0,00Within Groups 3104,00 30,00 103,47 Total 6917,90 39,00

Post Hoc Tests

Tamhane Gucci Miu-miu -2,00 0,89 0,97 -7,85 3,85Stuart Weitzman -1,50 2,70 1,00 -28,04 25,04

Brain Atwood -11,25 9,95 1,00 -132,02 109,52

Alexandra Mcqueen -4,75 4,73 1,00 -58,77 49,27

Walter Steiger -17,75 4,05 0,58 -62,69 27,19

Christian Louboutin -3,25 5,23 1,00 -63,75 57,25

Jimmy Choo

-2,75 2,17 1,00 -22,00 16,50

Manolo Blahnik -1,25 1,63 1,00 -13,18 10,68

Louis Voitton -32,50 9,09 0,81 -142,44 77,44

Miu-miu Gucci 2,00 0,89 0,97 -3,85 7,85Stuart Weitzman 0,50 2,64 1,00 -28,90 29,90

Brain Atwood -9,25 9,93 1,00 -131,14 112,64


Walter Steiger -15,75 4,01 0,72 -63,08 31,58


Jimmy Choo -0,75 2,10 1,00 -22,83 21,33

Manolo Blahnik 0,75 1,52 1,00 -13,37 14,87

Louis Voitton -30,50 9,07 0,86 -141,66 80,66

Stuart Weitzman

Gucci 1,50 2,70 1,00 -25,04 28,04Miu-miu -0,50 2,64 1,00 -29,90 28,90Brain Atwood -9,75 10,25 1,00 -114,20 94,70


Walter Steiger -16,25 4,75 0,56 -47,14 14,64


Jimmy Choo -1,25 3,30 1,00 -21,17 18,67

Manolo Blahnik 0,25 2,97 1,00 -20,71 21,21

Louis Voitton -31,00 9,42 0,82 -123,95 61,95

Brain Atwood

Gucci 11,25 9,95 1,00 -109,52 132,02Miu-miu 9,25 9,93 1,00 -112,64 131,14Stuart Weitzman 9,75 10,25 1,00 -94,70 114,20

Alexandra Mcqueen 6,50 10,97 1,00 -78,51 91,51

Walter Steiger -6,50 10,69 1,00 -96,98 83,98

Christian Louboutin 8,00 11,19 1,00 -73,90 89,90

Jimmy Choo 8,50 10,13 1,00 -101,82 118,82

Manolo Blahnik 10,00 10,02 1,00 -106,00 126,00

Louis Voitton -21,25 13,43 1,00 -99,76 57,26

Alexandra Mcqueen


Brain Atwood -6,50 10,97 1,00 -91,51 78,51

Walter Steiger -13,00 6,14 0,98 -49,29 23,29

Christian Louboutin

1,50 6,97 1,00 -39,29 42,29

Jimmy Choo 2,00 5,10 1,00 -39,22 43,22

Manolo Blahnik 3,50 4,89 1,00 -43,32 50,32

Louis Voitton -27,75 10,19 0,88 -102,82 47,32

Walter Steiger


Brain Atwood 6,50 10,69 1,00 -83,98 96,98



Jimmy Choo 15,00 4,47 0,66 -18,04 48,04

Manolo Blahnik 16,50 4,23 0,59 -21,20 54,20

Louis Voitton -14,75 9,89 1,00 -94,50 65,00

Christian Louboutin


Brain Atwood -8,00 11,19 1,00 -89,90 73,90


Walter Steiger -14,50 6,53 0,96 -54,16 25,16

Jimmy Choo 0,50 5,56 1,00 -46,97 47,97

Manolo Blahnik 2,00 5,37 1,00 -51,47 55,47

Louis Voitton -29,25 10,43 0,84 -101,86 43,36

Jimmy Choo


Brain Atwood -8,50 10,13 1,00 -118,82 101,82


Walter Steiger -15,00 4,47 0,66 -48,04 18,04


Manolo Blahnik 1,50 2,50 1,00 -14,18 17,18

Louis Voitton -29,75 9,29 0,86 -128,64 69,14

Manolo Blahnik

Gucci 1,25 1,63 1,00 -10,68 13,18Miu-miu -0,75 1,52 1,00 -14,87 13,37Stuart Weitzman -0,25 2,97 1,00 -21,21 20,71

Brain Atwood -10,00 10,02 1,00 -126,00 106,00

Alexandra Mcqueen

-3,50 4,89 1,00 -50,32 43,32

Walter Steiger -16,50 4,23 0,59 -54,20 21,20


Jimmy Choo -1,50 2,50 1,00 -17,18 14,18

Louis Voitton -31,25 9,17 0,83 -136,07 73,57

Louis Voitton


Brain Atwood 21,25 13,43 1,00 -57,26 99,76


Walter Steiger 14,75 9,89 1,00 -65,00 94,50


Jimmy Choo 29,75 9,29 0,86 -69,14 128,64

Manolo Blahnik 31,25 9,17 0,83 -73,57 136,07

As we can see from the tables null hypothesis can be rejected (ANOVA sig.<0.05) but we should conduct Post hoc test to identify which brand contributes the most share. According to Tamhane test it’s a Louis Voitton brand (differences are the largest).Outcome: all luxury brands contribute different share into a total qty of goods sold and Louis Voitton brand expels the others.

3. Multiple regression.Initial dataMonthly cost for advertising in newspapersMonthly cost for advertising on the radioSEO optimization costsPaper catalogues production costTotal marketing expendituresTasksFind out if above mentioned initiatives represent the major share of overall marketing expenditures, comprise a regression model, determine the major influencer.

Model Summaryb

Model R R SquareAdjusted R

SquareStd. Error of the

Estimate1 ,858a ,736 ,619 2670,01769

ANOVAa

Model Sum of Squares df Mean Square F Sig.1 Regression 179111192,822 4 44777798,206 6,281 ,011b

Residual 64160950,035 9 7128994,448

Total 243272142,857 13

Coefficientsa

Model

Unstandardized Coefficients

Stand. Coef

f.

tSig.

95,0% Confidence

Interval for B CorrelationsCollinearity Statistics

BStd. Error Beta

Lower Bound

Upper Bound

Zero-

order

Partial

Part

Tolerance VIF

1 (Constant) 38177,84

7632,55 5,0

00,0

020911,

8055443,

87

Advertizingjournals ,686 ,439 ,378 1,5

62,15

3 -,307 1,679 ,746 ,462 ,26

7 ,500 2,002

Advertizingradio ,297 ,213 ,403 1,3

94,19

7 -,185 ,779 ,782 ,421 ,23

9 ,350 2,856

Seocosts 1,188 1,553 ,173 ,765

,464 -2,326 4,702 ,59

2 ,247 ,131 ,574 1,7

41Papercatalogues -,211 ,248 -,14

9-,84

9,41

8 -,773 ,351 -,245

-,272

-,145 ,952 1,0

50

Collinearity Diagnosticsa

Model EigenvalueCondition

Index

Variance Proportions

(Constant) Advertizingjournals Advertizingradio Seocosts Papercatalogues1 1 4,771 1,000 ,00 ,00 ,00 ,00

2 ,147 5,689 ,01 ,04 ,20 ,003 ,051 9,639 ,00 ,71 ,19 ,134 ,025 13,783 ,00 ,13 ,49 ,685 ,005 29,887 ,99 ,12 ,11 ,19

Correlations

TotalmarketingcostAdvertizingjournal

s Advertizingradio Seocosts PapercataloguesPearson Correlation

Totalmarketingcost 1,000 ,746 ,782 ,592 -,245

Advertizingjournals ,746 1,000 ,691 ,381 -,153

Advertizingradio,782 ,691 1,000 ,639 -,046

Seocosts,592 ,381 ,639 1,000 -,116

Papercatalogues-,245 -,153 -,046 -,116 1,000

Sig. (1-tailed)

Totalmarketingcost ,001 ,000 ,013 ,199

Advertizingjournals ,001 ,003 ,090 ,301

Advertizingradio,000 ,003 ,007 ,438

Seocosts,013 ,090 ,007 ,347

Papercatalogues,199 ,301 ,438 ,347

N Totalmarketingcost 14 14 14 14 14

Advertizingjournals 14 14 14 14 14

Advertizingradio14 14 14 14 14

Seocosts14 14 14 14 14

Papercatalogues14 14 14 14 14

As we can see from the tables above multicollinearity is not observed (correlation coefficients between dependants are less than 0.7; tolerance more than 0.1, VIF less than 10); population is normal distributed (according to the plot); outliers are not identified (Cook’s distance is less that 1, case wise diagnostics didn’t show any outlier).

Outcome. All 4 predictors contribute significantly in overall marketing expenditures (determination coefficient=0,736: that means that regression model describes influence for 73.6%). Monthly cost for advertising in newspapers and monthly cost for advertising on the radio influence 3 times larger than SEO optimization cost on overall expenditures.

Regression model: y=38177+0.686x1+0.297x2+1,188x3-0.211x4 where

Y1- Total marketing expenditures

X1- Monthly cost for advertising in newspapers

X2- Monthly cost for advertising on the radio

X3- SEO optimization costs

X4- Paper catalogues production cost.

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